Runoff estimation using the SCS-CN method and GIS: a case study in the Wuseta watershed, upper blue Nile Basin, Ethiopia

Discover Water, Год журнала: 2025, Номер 5(1)

Опубликована: Апрель 24, 2025

Язык: Английский

River Surface Space–Time Image Velocimetry Based on Dual-Channel Residual Network

Applied Sciences, Год журнала: 2025, Номер 15(10), С. 5284 - 5284

Опубликована: Май 9, 2025

Space–Time Image Velocimetry (STIV) estimates the one-dimensional time-averaged velocity by analyzing main orientation of texture (MOT) in space–time images (STIs). However, environmental interference often blurs weak tracer textures STIs, limiting accuracy traditional MOT detection algorithms based on shallow features like images’ gray gradient. To solve this problem, we propose a deep learning-based model using dual-channel ResNet (DCResNet). The integrates and edge channels through ResNet18, performs weighted fusion extracted from two channels, finally outputs MOT. An adaptive threshold Sobel operator channel improves model’s ability to extract STI. Based typical mountainous river (located at Panzhihua hydrological station City, Sichuan Province), an STI dataset is constructed. DCResNet achieves optimal 7:3 gray–edge ratio, with MAEs 0.41° (normal scenarios) 1.2° (complex noise scenarios), respectively, outperforming single-channel models. In flow comparison experiments, demonstrates excellent performance robustness. Compared current meter results, MRE 4.08%, which better than FFT method.

Язык: Английский

Robust River Surface Velocity Measurement Based on Adaptive Masked Fft-Stivrobust River Surface Velocity Measurement Based on Adaptive Masked Fft-Stiv

Опубликована: Янв. 1, 2024

Язык: Английский